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Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions.

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    |July 12, 2018
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    Summary
    This summary is machine-generated.

    A new method, multiple-criteria decision analysis-multifactor dimensionality reduction (MCDA-MDR), enhances gene-gene interaction (GGI) detection for diseases. This approach improves upon traditional methods by using multiple criteria for more accurate GGI identification.

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    Area of Science:

    • Genetics
    • Bioinformatics
    • Computational Biology

    Background:

    • Gene-gene interactions (GGIs) are crucial for understanding disease susceptibility.
    • Multifactor dimensionality reduction (MDR) is a common algorithm for GGI detection, often relying solely on correct classification rate (CCR).
    • CCR alone may be insufficient for detecting GGIs due to model biases and disease complexity.

    Purpose of the Study:

    • To propose and evaluate a novel method, multiple-criteria decision analysis-multifactor dimensionality reduction (MCDA-MDR), for enhanced GGI detection.
    • To improve the accuracy and success rate of identifying GGIs compared to traditional MDR methods.

    Main Methods:

    • Developed MCDA-MDR by integrating multiple-criteria decision analysis (MCDA) with MDR.
    • MCDA-MDR simultaneously uses multiple measures, including CCR and rule utility, to assess GGIs within MDR's framework.
    • Employed cross-validation consistency to identify optimal GGIs from Pareto sets.

    Main Results:

    • Simulation studies demonstrated that MCDA-MDR achieved superior GGI detection success rates compared to MDR alone.
    • Analysis of the Wellcome Trust Case Control Consortium dataset identified numerous significant GGIs associated with coronary artery disease (p < 0.001) using MCDA-MDR.
    • Performance assessment confirmed that MCDA-MDR significantly enhanced GGI detection efficacy over MDR.

    Conclusions:

    • MCDA-MDR offers a more robust and successful approach for detecting gene-gene interactions relevant to complex diseases.
    • The integration of MCDA with MDR overcomes limitations of single-measure assessments, leading to improved GGI identification.
    • This method holds promise for advancing genetic research in disease susceptibility and personalized medicine.